Position Opening at HP Labs, Palo Alto: Deep Learning Scientist for 3D Printing

HP Labs’ 3D Lab (3DL) invents next generation 3D printing technology and novel materials, and creates their digital twins to enable digital manufacturing.  A key technical foundation that delivers precision manufacturing at voxel level such as HP’s Multi Jet Fusion technology is ubiquitous sensing for both machine and products, and process control based on continuous sensing data stream enriched by physics.  HP’s Multi Jet Fusion 3D printers have been in production worldwide; each production build generates terabytes of data from geometrical processing to in-situ thermal sensing. 3DL has created a highly promising research in applying deep learning to these datasets –  a single production build of terabytes,  different builds for a single machine, and different builds by different machines in different geographical location.  We are expanding this deep learning research project to accelerate our preliminary success.  We are inviting you to join our journey. 

We expect you an accomplished deep learning specialist, in particular,

1. PhD (preferred) in engineering and/or applied physics from reputable universities and research programs. Multiple years of independent research with demonstratable outcome.
2. Hands on experiences in deep learning; experiences with recurrent neural network (e.g., LSTM) is a plus. Experiences with generative adversarial networks and/or reinforcement learning a plus.
3. Hands on experiences in established platforms; Tensorflow experience is a plus.
4. Hands on experiences in applying deep learning to analyze sensing data in the form of images, videos.
5. Research experiences on applying deep learning to solve scientific problems; some hardware/physical science background is a plus. 
6. Willing to lead research; willing learn and teach.

More details can be found from
https://h30631.www3.hp.com/job/palo-alto/machine-learning-scientist-for-3d-printing-research/3544/13031902

  Contact:  Dr. Zeng (Email: jun.zeng@hp.com)

Summary of FACAM 2019 Workshop

Co-organized by Prof. N. Anwer (ENS Paris-Saclay), Prof. C. Mehdi-Souzani (ENS Paris-Sacaly), and Prof. Q. Huang (USC), the third FACAM 2019 workshop was held on June 17th and 18th at ENS Paris-Saclay (Cachan).  Other than organizers, the participants include Dr. AF Obaton (LNE of French Metrology Institute), Prof. J. Liu (University of Arizona), Prof. R. Jin (Virginia Tech), Prof. M. Grasso (Politecnico di Milano), and graduate students  Zuowei Zhu, Yahya Al Meslem, Kevin Pereirrai (ENS Paris-Saclay), Baltej Rupal (ENS Paris-Saclay/University of Alberta), Saint-Clair Toguem Tagne (LNE/ENS Paris-Saclay), Charles Cayron(LNE), Yuanxiang Wang, Nathan Decker, and Chris Henson (USC).

Prof. Anwer opened the workshop by welcoming everyone and gave an introduction of the Advanced and Smart Manufacturing research at the LURPA research lab and AM Initiatives at ENS Paris-Saclay. Dr. Obtaton’s presentation introduced metrology challenges in AM and opportunities for statistical analysis and machine learning of metrology data. Prof. Grasso presented novel solutions for in-situ monitoring of metal AM. Baltej Rupal discussed geometric Benchmark test artifacts design for GD&T quantification. Dr. Huang presented a new convolution framework for learning and predicting shape accuracy in AM. Zuowei Zhu and Kevin Ferreira presented a data-driven Skin Model Shapes  and deep learning for AM. Nathan Decker ended the first day by presenting dimensional accuracy prediction using machine learning of triangular mesh data.  

On the second day of the workshop Prof. Liu presented the spatio-temporal modeling of photothermal curing process for glass printing. Prof. Jin discussed  data fusion for quality modeling in AM processes and network. Yuanxiang Wang, Nathan Decker, and Chris Henson presented a software prototype demo of cloud-based machine learning for 3D printing distortion control. In the afternoon session, Prof. Huang led the discussion on the collaboration opportunities and new research problems arising from the presentations and follow-up questions.  Prof. C. Mehdi-Souzani, Prof. Liu, Prof. Grasso, and Prof. Jin made great remarks (Prof. Anwer left earlier due to a conference). A whitepaper is expected to produced from the discussions. Prof. C. Mehdi-Souzani gave a warm concluding remark.

The FACAM 2020 workshop will be in USC. Looking forward to another productive and fun AM workshop!

Contact: Dr. Q. Huang (qiang.huang@usc.edu)